OHO: A Multi-Modal, Multi-Purpose Dataset for Human-Robot Object Hand-Over.

In the context of collaborative robotics, handing over hand-held objects to a robot is a safety-critical task. Therefore, a robust distinction between human hands and presented objects in image data is essential to avoid contact with robotic grippers. To be able to develop machine learning methods for solving this problem, we created the OHO (Object Hand-Over) dataset of tools and other everyday objects being held by human hands.

Point Cloud Hand-Object Segmentation Using Multimodal Imaging with Thermal and Color Data for Safe Robotic Object Handover.

This paper presents an application of neural networks operating on multimodal 3D data (3D point cloud, RGB, thermal) to effectively and precisely segment human hands and objects held in hand to realize a safe human-robot object handover. We discuss the problems encountered in building a multimodal sensor system, while the focus is on the calibration and alignment of a set of cameras including RGB, thermal, and NIR cameras. We propose the use of a copper-plastic chessboard calibration target with an internal active light source (near-infrared and visible light).